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Oversmoothing in Graph Neural Networks (GNNs) refers to the phenomenon where increasing network depth leads to homogeneous node representations.
A note on the joint spectral radius
Gian-Carlo Rota and W. Gilbert Strang · 1960
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Sets of matrices all infinite products of which converge
Ingrid Daubechies and Jeffrey C. Lagarias · 1992
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The Lyapunov exponent and joint spectral radius of pairs of matrices are hard—when not impossible—to compute and to approximate
John N. Tsitsiklis and Vincent D. Blondel · 1997
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Nonhomogeneous Matrix Products
Darald J. Hartfiel · 2002
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Functional Analysis
Peter D. Lax · 2002
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Convergence in multiagent coordination, consensus, and flocking
Vincent D. Blondel, Julien M. Hendrickx, Alexander Olshevsky, and John N. Tsitsiklis · 2005
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A new model for learning in graph domains
M. Gori, G. Monfardini, and F. Scarselli · 2005
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Joint spectral radius: theory and approximations
Jacques Theys · 2005
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Markov Chains and Mixing Times
David A. Levin, Yuval Peres, and Elizabeth L. Wilmer · 2008
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Non-negative Matrices and Markov Chains
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The Joint Spectral Radius: Theory and Applications
Raphaël M. Jungers · 2009
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2009
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Spectral networks and locally connected networks on graphs
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Convolutional networks on graphs for learning molecular fingerprints
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Interaction networks for learning about objects, relations and physics
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov · 2016
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Neural message passing for quantum chemistry
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Semi-supervised classification with graph convolutional networks
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Attention is all you need
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Simple and deep graph convolutional networks
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li · 2020
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Bayesian graph neural networks with adaptive connection sampling
Arman Hasanzadeh, Ehsan Hajiramezanali, Shahin Boluki, Mingyuan Zhou, Nick G. Duffield, Krishna R. Narayanan, and Xiaoning Qian · 2020
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Scattering gcn: Overcoming oversmoothness in graph convolutional networks
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Graph neural networks exponentially lose expressive power for node classification
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Geom-gcn: Geometric graph convolutional networks
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Dropedge: Towards deep graph convolutional networks on node classification
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Graph attention networks
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Representation learning on graphs with jumping knowledge networks
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Graph convolutional policy network for goal-directed molecular graph generation
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Fast graph representation learning with PyTorch Geometric
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Predict then propagate: Graph neural networks meet personalized pagerank
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Diffusion improves graph learning
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Yu Rong, Wen bing Huang, Tingyang Xu, and Junzhou Huang · 2020
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Graph neural networks in recommender systems: A survey
Shiwen Wu, Wentao Zhang, Fei Sun, and Bin Cui · 2020
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Graphsaint: Graph sampling based inductive learning method
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Pairnorm: Tackling oversmoothing in gnns
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How attentive are graph attention networks?
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Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, and Aukosh Jagannath · 2022
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Feature overcorrelation in deep graph neural networks: A new perspective
Wei Jin, Xiaorui Liu, Yao Ma, Charu C. Aggarwal, and Jiliang Tang · 2022
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Not too little, not too much: a theoretical analysis of graph (over)smoothing
Nicolas Keriven · 2022
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Revisiting over-smoothing in bert from the perspective of graph
Han Shi, Jiahui Gao, Hang Xu, Xiaodan Liang, Zhenguo Li, Lingpeng Kong, Stephen M. S. Lee, and James Tin-Yau Kwok · 2022
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A survey on oversmoothing in graph neural networks
T.Konstantin Rusch, Michael M. Bronstein, and Siddhartha Mishra · 2023
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A non-asymptotic analysis of oversmoothing in graph neural networks
Xinyi Wu, Zhengdao Chen, William Wang, and Ali Jadbabaie · 2023
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